class: center, middle, inverse, title-slide # Geospatial Techniques for Social Scientists in R ## Applied Data Wrangling ### Stefan Jünger & Anne-Kathrin Stroppe<br>February 09, 2021 --- layout: true --- ## Now <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;"> Day </th> <th style="text-align:left;"> Time </th> <th style="text-align:left;"> Title </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;color: gray !important;"> February 08 </td> <td style="text-align:left;color: gray !important;"> 09:00am-10:30am </td> <td style="text-align:left;font-weight: bold;"> Introduction </td> </tr> <tr> <td style="text-align:left;color: gray !important;"> February 08 </td> <td style="text-align:left;color: gray !important;"> 11:00am-12:30pm </td> <td style="text-align:left;font-weight: bold;"> Vector Data </td> </tr> <tr> <td style="text-align:left;color: gray !important;color: gray !important;"> February 08 </td> <td style="text-align:left;color: gray !important;color: gray !important;"> 12:30pm-01:30pm </td> <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td> </tr> <tr> <td style="text-align:left;color: gray !important;"> February 08 </td> <td style="text-align:left;color: gray !important;"> 01:30pm-03:00pm </td> <td style="text-align:left;font-weight: bold;"> Basic Maps </td> </tr> <tr> <td style="text-align:left;color: gray !important;border-bottom: 1px solid"> February 08 </td> <td style="text-align:left;color: gray !important;border-bottom: 1px solid"> 03:30pm-05:00pm </td> <td style="text-align:left;font-weight: bold;border-bottom: 1px solid"> Raster Data </td> </tr> <tr> <td style="text-align:left;color: gray !important;"> February 09 </td> <td style="text-align:left;color: gray !important;"> 09:00am-10:30am </td> <td style="text-align:left;font-weight: bold;"> Advanced Data Import </td> </tr> <tr> <td style="text-align:left;color: gray !important;background-color: yellow !important;"> February 09 </td> <td style="text-align:left;color: gray !important;background-color: yellow !important;"> 11:00am-12:30pm </td> <td style="text-align:left;font-weight: bold;background-color: yellow !important;"> Applied Data Wrangling </td> </tr> <tr> <td style="text-align:left;color: gray !important;color: gray !important;"> February 09 </td> <td style="text-align:left;color: gray !important;color: gray !important;"> 12:30pm-13:30pm </td> <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td> </tr> <tr> <td style="text-align:left;color: gray !important;"> February 09 </td> <td style="text-align:left;color: gray !important;"> 01:30pm-03:00pm </td> <td style="text-align:left;font-weight: bold;"> Advanced Maps I </td> </tr> <tr> <td style="text-align:left;color: gray !important;"> February 09 </td> <td style="text-align:left;color: gray !important;"> 03:30pm-05:00pm </td> <td style="text-align:left;font-weight: bold;"> Advanced Maps II </td> </tr> </tbody> </table> --- ## What Are Georeferenced Data? .pull-left[ </br> Data with a direct spatial reference `\(\rightarrow\)` **geo-coordinates** - Information about geometries - Optional: Content in relation to the geometries ] .pull-right[ <img src="data:image/png;base64,#./img/fig_geometries.png" width="85%" style="display: block; margin: auto;" /> .tinyisher[Sources: OpenStreetMap / GEOFABRIK (2018), City of Cologne (2014), and the Statistical Offices of the Federation and the Länder (2016) / Jünger, 2019] ] --- ## Georeferenced Survey Data Survey data enriched with geo-coordinates (or other direct spatial references) </br> <img src="data:image/png;base64,#./img/geo_surveys.png" width="85%" style="display: block; margin: auto;" /> </br> .center[**With georeferenced survey data, we can analyze interactions between individual behaviors and attitudes and the environment.**] --- ## Prerequisite: Geocoding .pull-left[ Indirect spatial references have to be converted into direct spatial references `\(\rightarrow\)` Addresses to geo-coordinates Different service providers can be used - e.g., Google, Bing, OSM - In Germany: Federal Agency of Cartography and Geodesy (BKG) ] .pull-right[ </br> </br> <img src="data:image/png;base64,#./img/geocoding.png" width="785" style="display: block; margin: auto;" /> ] --- ## Georeferenced Survey Data `\(\neq\)` Geospatial Data .pull-left[ We must not store geo-coordinates and survey data in one dataset - Differences to geospatial data - More complicated workflow to work with (see Challenges) ] .pull-right[ <img src="data:image/png;base64,#./img/fig_linking_workflow_simple.png" width="85%" style="display: block; margin: auto;" /> .right[.tinyisher[Jünger, 2019]] ] --- ## Data Availability .pull-left[ Geospatial Data - Often de-centralized distributed - Fragmented data landscape, at least in Germany Georeferenced Survey Data - Primarily, survey data - Depends on documentation - Access difficult due to data protection restrictions ] .pull-right[ <img src="data:image/png;base64,#./img/data_availability.png" width="75%" style="display: block; margin: auto;" /> .right[.tinyisher[ https://www.eea.europa.eu/data-and-maps https://datasearch.gesis.org/ https://datasetsearch.research.google.com/ ]] ] --- ## Technical Procedures .pull-left[ </br> .center[<img src="./img/angry_cat.gif" width="75%">] .center[.tinyisher[https://giphy.com/gifs/VbnUQpnihPSIgIXuZv]] ] .pull-right[ Geocoding - Reasonable automated procedure - But differ in quality and access rights - High risk for data protection GIS procedures - Requires exploiting specialized software - Can get complex and resource-intensive ] --- ## Data Protection </br> </br> That‘s one of the biggest issues - Explicit spatial references increase the risk of re-identifying anonymized survey respondents - Can occur during the processing of data but also during the analysis </br> .center[**Affects all phases of research and data management!**] --- ## Legal Regulations .pull-left[ Storing personal information such as addresses in the same place as actual survey attributes is not allowed in Germany - Projects keep them in separate locations - Can only be matched with a correspondence table - Necessary to conduct data linking ] .pull-right[ <img src="data:image/png;base64,#./img/fig_linking_workflow_simple.png" width="949" style="display: block; margin: auto;" /> .right[.tinyisher[Jünger, 2019]] ] --- ## Distribution & Re-Identification Risk Data may still be sensitive - Geospatial attributes add new information to existing data - May be part of general data privacy checks, but we may not distribute these data as is .pull-left[ Safe Rooms / Secure Data Centers - Control access - Checks output ] .pull-right[ <img src="data:image/png;base64,#./img/safe_room.png" width="825" style="display: block; margin: auto;" /> .right[.tinyisher[https://www.gesis.org/en/services/processing-and-analyzing-data/guest-research-stays/secure-data-center-sdc]] ] --- ## Spatial Linking <img src="data:image/png;base64,#./img/fig_3d_.png" width="45%" style="display: block; margin: auto;" /> .tinyisher[Sources: OpenStreetMap / GEOFABRIK (2018), City of Cologne (2014), Leibniz Institute of Ecological Urban and Regional Development (2018), Statistical Offices of the Federation and the Länder (2016), and German Environmental Agency / EIONET Central Data Repository (2016) / Jünger, 2019] --- ## Spatial Linking Methods (Examples) I .pull-left[ 1:1 <img src="data:image/png;base64,#./img/fig_linking_by_location_noise.png" width="75%" style="display: block; margin: auto;" /> ] .pull-right[ Distances <img src="data:image/png;base64,#./img/fig_linking_distance_noise_appI.png" width="75%" style="display: block; margin: auto;" /> ] .tinyisher[Sources: German Environmental Agency / EIONET Central Data Repository (2016) and OpenStreetMap / GEOFABRIK (2018) / Jünger, 2019] --- ## Spatial Linking Methods (Examples) II .pull-left[ Filter methods <img src="data:image/png;base64,#./img/fig_linking_focal_immigrants.png" width="75%" style="display: block; margin: auto;" /> ] .pull-right[ Buffer zones <img src="data:image/png;base64,#./img/fig_linking_buffer_sealing.png" width="75%" style="display: block; margin: auto;" /> ] .tinyisher[Sources: Leibniz Institute of Ecological Urban and Regional Development (2018) and Statistical Offices of the Federation and the Länder (2016) / Jünger, 2019] --- ## Fake Research Question .pull-left[ Say we're interested in the impact of the current pandemic on individual well-being in the geographic context. We plan to conduct a survey in the state of North-Rhine Westphalia. ] .pull-right[ </br> <img src="data:image/png;base64,#./img/4iq3kg.jpg" width="813" style="display: block; margin: auto;" /> .center[.tinyisher[https://imgflip.com/memegenerator/Trump-Bill-Signing] ] ] --- ## Our Sample Area: NRW’s Boundaries .pull-left[ ```r nrw <- osmdata::getbb( "Nordrhein-Westfalen", format_out = "sf_polygon" ) %>% .$multipolygon %>% sf::st_transform(3035) ``` ] -- .pull-right[ ```r tm_shape(nrw) + tm_borders() ``` <img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/nrw-map-1.png" style="display: block; margin: auto;" /> ] --- ## A Fake-Life Application .pull-left[ Let's sample 1,000 people to interview them about their lives. We can draw a fake sample this way and also add an identifier for the respondents: ```r set.seed(1234) ``` ```r fake_coordinates <- sf::st_sample(nrw, 1000) %>% sf::st_sf() %>% dplyr::mutate( id_2 = stringi::stri_rand_strings( 10000, 10 ) %>% sample(1000, replace = FALSE) ) ``` ] -- .pull-right[ ```r tm_shape(nrw) + tm_borders() + tm_shape(fake_coordinates) + tm_dots() ``` <img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/map-osm-coordinates-1.png" style="display: block; margin: auto;" /> ] --- ## Correspondence Table As in any survey that deals with addresses, we need a correspondence table of the distinct identifiers. ```r correspondence_table <- dplyr::bind_cols( id = stringi::stri_rand_strings(10000, 10) %>% sample(1000, replace = FALSE), id_2 = fake_coordinates$id_2 ) correspondence_table ``` ``` ## # A tibble: 1,000 x 2 ## id id_2 ## <chr> <chr> ## 1 ubsHG5McEM ihkXs9ejBD ## 2 WN7Ih0Y5Rz bN2W1BpZKx ## 3 lmLdwfl3cu wAbDkMovWz ## 4 uq2Rb6Dj2w R4eIulul4z ## 5 y7eYFQSuP3 XOvF2ZuGg1 ## 6 UxERvtP2Kx EPuILKVeoq ## 7 67N3O8FPyO 39TfAAxmme ## 8 I0AUhXMPkD 2tolhpgrNl ## 9 41h2EPFU1S nGgofAl6iC ## 10 9YaVHR70jt 4sXgiH1ydA ## # ... with 990 more rows ``` --- ## Conduct the Survey We ask respondents for some standard sociodemographics. But we also apply a new and highly innovative item score, called the Fake Corona Burden Score (FCBS) using the [`faux` package](https://cran.r-project.org/web/packages/faux/index.html). ```r fake_survey_data <- dplyr::bind_cols( id = correspondence_table$id, age = sample(18:100, 1000, replace = TRUE), gender = sample(1:2, 1000, replace = TRUE) %>% as.factor(), education = sample(1:4, 1000, replace = TRUE) %>% as.factor(), income = sample(100:10000, 1000, replace = TRUE), fcbs = secret_variable_we_are_hiding_from_you ) ``` --- ## Survey Data Structure ```r fake_survey_data ``` ``` ## # A tibble: 1,000 x 6 ## id age gender education income fcbs ## <chr> <int> <fct> <fct> <int> <dbl> ## 1 ubsHG5McEM 72 2 1 6061 64.5 ## 2 WN7Ih0Y5Rz 49 1 3 4548 61.7 ## 3 lmLdwfl3cu 84 1 4 6850 49.4 ## 4 uq2Rb6Dj2w 90 1 4 1186 59.4 ## 5 y7eYFQSuP3 88 2 2 5888 68.6 ## 6 UxERvtP2Kx 58 2 1 9210 51.3 ## 7 67N3O8FPyO 90 2 3 789 61.1 ## 8 I0AUhXMPkD 45 1 4 1925 48.3 ## 9 41h2EPFU1S 36 1 3 9587 35.6 ## 10 9YaVHR70jt 98 2 2 4455 28.8 ## # ... with 990 more rows ``` --- ## What could explain our Fake Corona Burden Score? *Likelihood to meet people* > 1) The lower the district's population density, the lower the Fake Corona Burden Score. -- *District relatively high affected by Covid-19* > 2) If the Corona 7 day incidence per 100k in the district is higher than in the state, the lower the Fake Corona Burden Score. --- ## What could explain our Fake Corona Burden Score? *Provision of health services* > 3) The closer the next hospital to the respondent, the lower the Fake Corona Burden Score. -- *Possible language issues in health care communication* > 3) The higher the immigrant rate in the neighborhood, the higher the Fake Corona Burden Score. --- ## Variable Overview Good Thing: We have all the data to calculate these indicators, even though we need first to transform some of the variables. 1. Calculate the variables at the district and state levels. 2. Link the district and state-level data with the fake survey respondents. 3. Calculate the hospital distance and immigrant rate. <img src="data:image/png;base64,#./img/overview_table.png" width="90%" style="display: block; margin: auto;" /> --- ## Population Density When all data sets are loaded, we can calculate the districts' area for the population density. <small><small>Remember: We reduced our sample to North Rhine Westphalia and dropped some variables.</small></small> .smaller[ ```r # calculate area of districts st_area(nrw_districts_enhanced) %>% head(4) ``` ``` ## Units: [m^2] ## [1] 217485313 233089701 210506541 136632876 ``` ```r # areas will always be calculated # in units according to the CRS nrw_districts_enhanced %>% mutate(area = st_area(.)) %>% dplyr::select(., area) ``` ``` ## Simple feature collection with 53 features and 1 field ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 4031313 ymin: 3029642 xmax: 4283898 ymax: 3269981 ## projected CRS: ETRS89-extended / LAEA Europe ## First 10 features: ## area geometry ## 1 217485313 [m^2] MULTIPOLYGON (((4094868 314... ## 2 233089701 [m^2] MULTIPOLYGON (((4092415 316... ## 3 210506541 [m^2] MULTIPOLYGON (((4114154 316... ## 4 136632876 [m^2] MULTIPOLYGON (((4080863 314... ## 5 170738940 [m^2] MULTIPOLYGON (((4075096 313... ## 6 91263039 [m^2] MULTIPOLYGON (((4105756 315... ## 7 77467352 [m^2] MULTIPOLYGON (((4103124 316... ## 8 74074591 [m^2] MULTIPOLYGON (((4128163 312... ## 9 88994505 [m^2] MULTIPOLYGON (((4117277 312... ## 10 168392652 [m^2] MULTIPOLYGON (((4130282 313... ``` ] --- ## Population Density All left to do is a simple mutation. Let's pipe it! .pull-left[ ```r # calculation population density nrw_districts_enhanced <- nrw_districts_enhanced %>% # calculate area mutate(area = st_area(.)) %>% # change unit to square kilometers mutate(area_km2 = units::set_units (area, km^2)) %>% # recode variable as numeric mutate(area_km2 = as.numeric (area_km2)) %>% # calculate population density mutate(pop_dens = population/ area_km2) ``` ] .pull-right[ <img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/unnamed-chunk-1-1.png" style="display: block; margin: auto;" /> ] --- ## Aggregate Covid-19 Cases In order to calculate the number of Covid-19 cases on the state level, we need to aggregate these numbers. Yes, we are overcomplicating things a little bit because we reduced our sample to one state. However, we're not going to miss this learning opportunity, right? You can also use this code and swap the "NRW"-sample with the sample for all of Germany. .pull-left[ ```r # check data sets nrw_districts_enhanced ``` ``` ## Simple feature collection with 53 features and 6 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 4031313 ymin: 3029642 xmax: 4283898 ymax: 3269981 ## projected CRS: ETRS89-extended / LAEA Europe ## First 10 features: ## district_id population cases_7days geometry ## 1 5111 621877 457 MULTIPOLYGON (((4094868 314... ## 2 5112 498686 403 MULTIPOLYGON (((4092415 316... ## 3 5113 582760 629 MULTIPOLYGON (((4114154 316... ## 4 5114 227417 225 MULTIPOLYGON (((4080863 314... ## 5 5116 261034 213 MULTIPOLYGON (((4075096 313... ## 6 5117 170632 104 MULTIPOLYGON (((4105756 315... ## 7 5119 210764 215 MULTIPOLYGON (((4103124 316... ## 8 5120 111338 76 MULTIPOLYGON (((4128163 312... ## 9 5122 159245 141 MULTIPOLYGON (((4117277 312... ## 10 5124 355100 442 MULTIPOLYGON (((4130282 313... ## area area_km2 pop_dens ## 1 217485313 [m^2] 217.48531 2859.398 ## 2 233089701 [m^2] 233.08970 2139.460 ## 3 210506541 [m^2] 210.50654 2768.370 ## 4 136632876 [m^2] 136.63288 1664.438 ## 5 170738940 [m^2] 170.73894 1528.849 ## 6 91263039 [m^2] 91.26304 1869.673 ## 7 77467352 [m^2] 77.46735 2720.682 ## 8 74074591 [m^2] 74.07459 1503.053 ## 9 88994505 [m^2] 88.99450 1789.380 ## 10 168392652 [m^2] 168.39265 2108.762 ``` ] -- .pull-right[ ```r # check data sets nrw_enhanced ``` ``` ## Simple feature collection with 1 feature and 2 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 4031313 ymin: 3029642 xmax: 4283898 ymax: 3269981 ## projected CRS: ETRS89-extended / LAEA Europe ## state_id population geometry ## 1 05 17912134 MULTIPOLYGON (((4052003 305... ``` ] --- ## Calculate Districts Within States We rely here on the `sf` feature `st_join()` and the `dplyr` functions `group by()`and `summarize()`. First step is a join of two sf objects. You always join based on the first-named object (x lying within which y?). ```r # step one: district in st_join(nrw_districts_enhanced, nrw_enhanced, join = st_within) %>% head(.,2) ``` ``` ## Simple feature collection with 2 features and 8 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 4086321 ymin: 3117194 xmax: 4107526 ymax: 3166283 ## projected CRS: ETRS89-extended / LAEA Europe ## district_id population.x cases_7days area area_km2 pop_dens ## 1 5111 621877 457 217485313 [m^2] 217.4853 2859.398 ## 2 5112 498686 403 233089701 [m^2] 233.0897 2139.460 ## state_id population.y geometry ## 1 05 17912134 MULTIPOLYGON (((4094868 314... ## 2 05 17912134 MULTIPOLYGON (((4092415 316... ``` --- ## A Word on Spatial Relations Every time we want to use a spatial join, meaning matching two spatial objects based on their geometric relation, we need to think about the type of relation we assume. `st_join` asks us to define the "join" type (default is intersect). <img src="data:image/png;base64,#./img/spatial_relations.png" width="90%" style="display: block; margin: auto;" /> .center[ <small><small>https://www.e-education.psu.edu/maps/l2_p5.html</small></small> ] --- ## Group and Sum on State-Level Back to our spatial join. After joining the information in which state each district lies (surprise: North Rhine Westphalia!), we can summarize the number of Covid-19 cases by grouping them on the state level. ```r # second step: group & sum st_join(nrw_districts_enhanced, nrw_enhanced, join = st_within) %>% group_by(state_id) %>% summarize(cases_7days_sta = sum(cases_7days)) %>% head(.,2) ``` ``` ## Simple feature collection with 1 feature and 2 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 4031313 ymin: 3029642 xmax: 4283898 ymax: 3269981 ## projected CRS: ETRS89-extended / LAEA Europe ## # A tibble: 1 x 3 ## state_id cases_7days_sta geometry ## <chr> <int> <MULTIPOLYGON [m]> ## 1 05 15494 (((4052003 3052536, 4052013 3052424, 4051753 3052420~ ``` --- # Final Steps In the last step, we drop the geometries, re-join our variable back to the state data frame and calculate our "7 day per 100k"-incidence. And - voilà - a simple function will give us our district differences. .pull-left[ ```r # the 'dplyr way' in completion nrw_enhanced <- st_join(nrw_districts_enhanced, nrw_enhanced, join = st_within) %>% group_by(state_id) %>% summarize(cases_7days_sta = sum(cases_7days)) %>% # already have geometries, so drop them st_drop_geometry() %>% # simple left_join left_join(nrw_enhanced,.) %>% # per 100k mutate(incidence_state = (cases_7days_sta/population)*100000) # calculate the difference between # state and district nrw_districts_enhanced <- nrw_districts_enhanced %>% mutate(incidence_diff = ((cases_7days/population)*100000) - nrw_enhanced$incidence) ``` ] .pull-right[ <img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/incidence-1.png" style="display: block; margin: auto;" /> ] --- ## You can do the same by using `aggregate` The alternative which you will probably come across is `aggregate`. It works a similar way, but it will directly create an `sf` object on the state level and perform the same and limited functions of aggregation to all columns in the data object. The definition of the assumed spatial relation is based on `sf`. I would recommend you to use st_join because it gives you more freedom in summarizing several variables with different functions (sum, mean, median, count, ...) at the same time. ```r aggregate(x = nrw_districts_enhanced, by = nrw_enhanced, FUN = sum, join = st_intersects) ``` --- ## Respondents in Districts We have population density and the difference in 7-day incidences on the district level. Since our analysis focuses on the individual-level, we can spatial join the information to our fake respondents' coordinates. ```r # join back spatial_information <- nrw_districts_enhanced %>% # keeping just the variables we want dplyr::select(., district_id, pop_dens, incidence_diff) %>% # since we want to join district to # respondent defining coordintes first st_join(fake_coordinates, # district data second . , # some points may lie on the border # choosing intersects therefore join = st_intersects) %>% # drop our coordinates for data protection st_drop_geometry() ``` --- ## Respondents in Districts ```r spatial_information ``` ``` ## id_2 district_id pop_dens incidence_diff ## 1 ihkXs9ejBD 5166 530.3000 -0.1728428 ## 2 bN2W1BpZKx 5570 210.6921 20.7559518 ## 3 wAbDkMovWz 5958 132.5497 -2.5818931 ## 4 R4eIulul4z 5570 210.6921 20.7559518 ## 5 XOvF2ZuGg1 5966 188.2262 -8.1154907 ## 6 EPuILKVeoq 5978 727.5190 4.6643744 ## 7 39TfAAxmme 5958 132.5497 -2.5818931 ## 8 2tolhpgrNl 5962 386.5767 16.6148759 ## 9 nGgofAl6iC 5119 2720.6816 15.5098081 ## 10 4sXgiH1ydA 5315 2675.5176 -7.1701807 ## 11 zF5RjK8V7v 5766 278.9389 23.7113643 ## 12 jOO2VHwycb 5170 441.2735 -14.1049414 ## 13 aM5jtujeJR 5162 783.2699 -11.8979375 ## 14 RFqtgUJmqR 5378 646.4502 4.5788380 ## 15 8QYMhEdKUV 5358 281.3451 19.3048880 ## 16 9MTwgPDLpH 5366 154.9113 -21.9525779 ## 17 30Bi23QI6u 5566 249.6467 -29.8314226 ## 18 JtRdole7nK 5766 278.9389 23.7113643 ## 19 R3lbT7Rruc 5913 2103.1495 -8.9819608 ## 20 NrGmMnwYQR 5112 2139.4596 -5.6876479 ## 21 WfIXk6eiST 5382 521.3532 -21.2497748 ## 22 M19UVtRjIp 5962 386.5767 16.6148759 ## 23 wWE2klWU84 5958 132.5497 -2.5818931 ## 24 g1YZKCfom9 5170 441.2735 -14.1049414 ## 25 Ctpu1AkZNX 5966 188.2262 -8.1154907 ## 26 tvsw0DcPnL 5754 376.4177 7.2145259 ## 27 y3jQ6WZ0MJ 5554 261.2675 -29.6786278 ## 28 pHsLA8ji19 5966 188.2262 -8.1154907 ## 29 zJIDJ2hsSr 5562 806.6064 17.5484224 ## 30 E311lYIbSG 5374 296.2820 12.3763156 ## 31 nAuSmBzE0e 5970 244.2126 -9.5891672 ## 32 5cuKs37UfT 5962 386.5767 16.6148759 ## 33 52bsUCSzrI 5362 669.2841 -24.4535517 ## 34 fTiCkROohX 5770 269.4442 17.2340828 ## 35 hp5sYBnxaz 5754 376.4177 7.2145259 ## 36 1foj1hkWPR 5962 386.5767 16.6148759 ## 37 lRagmzRqTk 5358 281.3451 19.3048880 ## 38 mM6EWST3C3 5978 727.5190 4.6643744 ## 39 x7RBN9jtO7 5358 281.3451 19.3048880 ## 40 jDtxOGb0XC 5366 154.9113 -21.9525779 ## 41 QbyUdmkLSQ 5554 261.2675 -29.6786278 ## 42 X62irYEpgw 5515 1038.6794 -53.5148313 ## 43 igjSIMzcXl 5974 226.9904 -15.2572507 ## 44 cY4ZT6M4FA 5334 790.8127 -3.0209752 ## 45 WbJnTxQLDi 5766 278.9389 23.7113643 ## 46 TrGRGMLbrI 5166 530.3000 -0.1728428 ## 47 laDf5yuNsA 5374 296.2820 12.3763156 ## 48 Wm037rL6lF 5362 669.2841 -24.4535517 ## 49 KqRssAE9Qz 5766 278.9389 23.7113643 ## 50 qCtSpVhAGN 5558 198.3087 -48.8729749 ## 51 jbjezVYKW8 5554 261.2675 -29.6786278 ## 52 tEZGTCmm99 5116 1528.8487 -4.9014571 ## 53 ESJLpnbjwu 5154 253.3367 -13.5318504 ## 54 fgmbUoOgj9 5770 269.4442 17.2340828 ## 55 nOZWDKTT3x 5554 261.2675 -29.6786278 ## 56 Et637ljumR 5554 261.2675 -29.6786278 ## 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808 24lKDS9pli 5122 1789.3801 2.0427886 ## 809 KERlPe46kL 5970 244.2126 -9.5891672 ## 810 6WJT9AjHfD 5911 2524.4746 1.8510400 ## 811 DzYg6fAR0B 5570 210.6921 20.7559518 ## 812 siVRTULbGb 5366 154.9113 -21.9525779 ## 813 hvgywwrA33 5558 198.3087 -48.8729749 ## 814 LaD3MBfLIM 5366 154.9113 -21.9525779 ## 815 f0MERx59xt 5962 386.5767 16.6148759 ## 816 YPXJVnEn3Q 5762 116.8019 69.6485964 ## 817 5qrvPZYZDk 5170 441.2735 -14.1049414 ## 818 mQmCG2RcpJ 5766 278.9389 23.7113643 ## 819 m57rZwDnFC 5316 2078.1543 13.0547292 ## 820 HQ4qsO0Cgv 5570 210.6921 20.7559518 ## 821 KwEj2vl97n 5170 441.2735 -14.1049414 ## 822 rCiUhEsiAh 5970 244.2126 -9.5891672 ## 823 N0LRKQ46jk 5962 386.5767 16.6148759 ## 824 4vqPvlYCD6 5962 386.5767 16.6148759 ## 825 6l1907gSGE 5166 530.3000 -0.1728428 ## 826 FAzeWuikpD 5170 441.2735 -14.1049414 ## 827 hFpMIKVhzc 5770 269.4442 17.2340828 ## 828 46AOqEHD0I 5958 132.5497 -2.5818931 ## 829 sbKs1GAd4L 5762 116.8019 69.6485964 ## 830 SxcbGBL1WV 5554 261.2675 -29.6786278 ## 831 ldJWFsvkqO 5378 646.4502 4.5788380 ## 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904 TJx9QwLOu7 5974 226.9904 -15.2572507 ## 905 0qMELx3QiE 5766 278.9389 23.7113643 ## 906 HtLiNitZvQ 5558 198.3087 -48.8729749 ## 907 tP3qFWKTuZ 5970 244.2126 -9.5891672 ## 908 3atHcY2dL7 5358 281.3451 19.3048880 ## 909 liAQQ6ZmcG 5970 244.2126 -9.5891672 ## 910 UuXAD5puEy 5570 210.6921 20.7559518 ## 911 PkJBUT4iOj 5962 386.5767 16.6148759 ## 912 9tzWttrZYk 5378 646.4502 4.5788380 ## 913 3N0z13pwpo 5915 790.3722 15.2141104 ## 914 LwOLYgPKn2 5366 154.9113 -21.9525779 ## 915 tr5WAu7Xr6 5154 253.3367 -13.5318504 ## 916 QHVZW8f8kX 5158 1190.5861 18.5311776 ## 917 u78bZeT98m 5958 132.5497 -2.5818931 ## 918 KcKtoyeovo 5334 790.8127 -3.0209752 ## 919 uobIFxoeXZ 5166 530.3000 -0.1728428 ## 920 aiIrpFYNtI 5762 116.8019 69.6485964 ## 921 sxDTqTCNBv 5154 253.3367 -13.5318504 ## 922 IljHbioO8X 5916 3037.6698 26.6358873 ## 923 I6s4c10Gvu 5166 530.3000 -0.1728428 ## 924 rVXPX5G4qG 5913 2103.1495 -8.9819608 ## 925 O8RJXYVKE2 5966 188.2262 -8.1154907 ## 926 SyrSiTRxIo 5958 132.5497 -2.5818931 ## 927 ED0rITjtZk 5554 261.2675 -29.6786278 ## 928 5LMMVpXSSq 5374 296.2820 12.3763156 ## 929 nHjfhQpdBV 5366 154.9113 -21.9525779 ## 930 vZG3pKeKN3 5966 188.2262 -8.1154907 ## 931 xsc1Lg6QR9 5382 521.3532 -21.2497748 ## 932 dPQLuHeQ4O 5566 249.6467 -29.8314226 ## 933 f9f9br6Dty 5958 132.5497 -2.5818931 ## 934 IF5nfQCY0S 5366 154.9113 -21.9525779 ## 935 74Wgs7V8Id 5114 1664.4384 12.4371718 ## 936 t1Oi2hYNS7 5116 1528.8487 -4.9014571 ## 937 BkfdGqu6Xf 5570 210.6921 20.7559518 ## 938 pL9N37lH5a 5382 521.3532 -21.2497748 ## 939 jl6Y7ld0Gw 5770 269.4442 17.2340828 ## 940 Gl136X2y6n 5978 727.5190 4.6643744 ## 941 aNwh9d5c4t 5558 198.3087 -48.8729749 ## 942 u8krkt1ubx 5978 727.5190 4.6643744 ## 943 iBDKlffMBB 5770 269.4442 17.2340828 ## 944 8xzkMJEWGs 5382 521.3532 -21.2497748 ## 945 HhwaLtZEBF 5382 521.3532 -21.2497748 ## 946 dAOKhTqeK1 5358 281.3451 19.3048880 ## 947 DyvoL79t7O NA NA NA ## 948 D8Nun56qoj 5766 278.9389 23.7113643 ## 949 wGOP559qph 5366 154.9113 -21.9525779 ## 950 j4ubHe5fSt 5358 281.3451 19.3048880 ## 951 YfBkzDDuY6 5762 116.8019 69.6485964 ## 952 LHQR1nCAbt 5762 116.8019 69.6485964 ## 953 oELEHhj87v 5358 281.3451 19.3048880 ## 954 tBPUtkQ0qE 5762 116.8019 69.6485964 ## 955 pNvmyWNkmZ 5754 376.4177 7.2145259 ## 956 qWdhS7Xj7F 5382 521.3532 -21.2497748 ## 957 E0BOT8GsN6 5562 806.6064 17.5484224 ## 958 08d9EC2QZ3 5558 198.3087 -48.8729749 ## 959 2LZ7l0LhnW 5358 281.3451 19.3048880 ## 960 ex73h9S17y 5962 386.5767 16.6148759 ## 961 oezaHPyXWl 5558 198.3087 -48.8729749 ## 962 RtaGSe88PK 5962 386.5767 16.6148759 ## 963 MEKXQIfsYu 5554 261.2675 -29.6786278 ## 964 h1HPgfUL2y 5366 154.9113 -21.9525779 ## 965 DiE1B7Az12 5358 281.3451 19.3048880 ## 966 STyqN4kS1Y 5374 296.2820 12.3763156 ## 967 vp3zRsBntg 5162 783.2699 -11.8979375 ## 968 RvWSGgDNCD 5554 261.2675 -29.6786278 ## 969 7q6SXwKwGn 5558 198.3087 -48.8729749 ## 970 5n65uwp5pU 5366 154.9113 -21.9525779 ## 971 LFQdbEKsl3 5962 386.5767 16.6148759 ## 972 T0dQKhHcFG 5970 244.2126 -9.5891672 ## 973 T3mTd0VYS7 5315 2675.5176 -7.1701807 ## 974 wcezPPOd3v 5113 2768.3700 21.4346329 ## 975 8b03Seyy45 5958 132.5497 -2.5818931 ## 976 a8zqT6NHw5 5566 249.6467 -29.8314226 ## 977 fOAmRBv1zR 5374 296.2820 12.3763156 ## 978 hYQHVYc4Jp 5374 296.2820 12.3763156 ## 979 uMqUvaRdFg 5374 296.2820 12.3763156 ## 980 1eO16GeSfu 5754 376.4177 7.2145259 ## 981 i0NB6AniWd 5170 441.2735 -14.1049414 ## 982 LD1keBoMUu 5158 1190.5861 18.5311776 ## 983 XtaxYnU1eY 5566 249.6467 -29.8314226 ## 984 ZArHUUJ7jI 5111 2859.3977 -13.0128220 ## 985 I77y8si0lg 5562 806.6064 17.5484224 ## 986 i0Mhtd9Rx7 5970 244.2126 -9.5891672 ## 987 Jpm0rsrcDG 5166 530.3000 -0.1728428 ## 988 eXrE1AZZzy 5554 261.2675 -29.6786278 ## 989 3FB3f1SUrS 5382 521.3532 -21.2497748 ## 990 pKU8Im7age 5970 244.2126 -9.5891672 ## 991 bSbsFdP6gB 5382 521.3532 -21.2497748 ## 992 41HhkMpPbV 5366 154.9113 -21.9525779 ## 993 iE0YIPSsFm 5570 210.6921 20.7559518 ## 994 ASrbQ4LI3s 5515 1038.6794 -53.5148313 ## 995 PIXGqSDFFz 5711 1290.0100 -16.7802485 ## 996 thkMlFVMSD 5958 132.5497 -2.5818931 ## 997 ZCvZTgeavE 5762 116.8019 69.6485964 ## 998 f2HtHf2pMN 5558 198.3087 -48.8729749 ## 999 qLK3GWBg13 5374 296.2820 12.3763156 ## 1000 OUVpAPPYxt 5958 132.5497 -2.5818931 ``` --- ## Distance to Closest Hospital .pull-left[ We're getting a little bit more advanced here. Not in the means of spatial relations or calculations, but concerning data wrangling in general. We got our survey respondents and already matched the population density and the difference of Corona incidences between state and district level. We want to calculate the straight line distance between him/her and the closest hospital for each of our fake respondents. ] .pull-right[ <img src="data:image/png;base64,#./img/distance.png" width="65%" style="display: block; margin: auto;" /> ] --- # Distance Calculation `st_distance` will calculate between **all** respondents and **all** hospitals resulting in a matrix with 344000 objects (1000 respondent * 344 hospitals). We can make our lives a little bit easier by treating this matrix as a `tibble` or data frame having 1000 observations with 344 variables. .pull-left[ ```r # distances between each respondent # and each hospital distance_matrix <- # point layer "distance from" st_distance(fake_coordinates, # point layer "distance to" nrw_hospitals, # dense matrix with all # pairwise distance by_element = FALSE) %>% # making life a little bit easier as_tibble() # check our matrix # again, units = CRS units! distance_matrix ``` ] .pull-right[ ``` ## # A tibble: 1,000 x 344 ## V1 V2 V3 V4 V5 V6 V7 V8 ## [m] [m] [m] [m] [m] [m] [m] [m] ## 1 37495.~ 37750.~ 45212.~ 34365.~ 39892.~ 41654.~ 37665.~ 40155.~ ## 2 127034.~ 125620.~ 126682.~ 121452.~ 120249.~ 121222.~ 124628.~ 122946.~ ## 3 90559.~ 89696.~ 85563.~ 90923.~ 86054.~ 85069.~ 89309.~ 86845.~ ## 4 123864.~ 122467.~ 123260.~ 118557.~ 117136.~ 118012.~ 121498.~ 119758.~ ## 5 98362.~ 97874.~ 91666.~ 101404.~ 95524.~ 93751.~ 97823.~ 95310.~ ## 6 73491.~ 72281.~ 71002.~ 70769.~ 67507.~ 67543.~ 71542.~ 69384.~ ## 7 110942.~ 110237.~ 105104.~ 112429.~ 107110.~ 105782.~ 109992.~ 107483.~ ## 8 75086.~ 74140.~ 70636.~ 74869.~ 70242.~ 69448.~ 73676.~ 71254.~ ## 9 37755.~ 36312.~ 42729.~ 28416.~ 31427.~ 34111.~ 35085.~ 34959.~ ## 10 37027.~ 38158.~ 30326.~ 46958.~ 41978.~ 39045.~ 39276.~ 38573.~ ## # ... with 990 more rows, and 336 more variables: V9 [m], V10 [m], V11 [m], ## # V12 [m], V13 [m], V14 [m], V15 [m], V16 [m], V17 [m], V18 [m], V19 [m], ## # V20 [m], V21 [m], V22 [m], V23 [m], V24 [m], V25 [m], V26 [m], V27 [m], ## # V28 [m], V29 [m], V30 [m], V31 [m], V32 [m], V33 [m], V34 [m], V35 [m], ## # V36 [m], V37 [m], V38 [m], V39 [m], V40 [m], V41 [m], V42 [m], V43 [m], ## # V44 [m], V45 [m], V46 [m], V47 [m], V48 [m], V49 [m], V50 [m], V51 [m], ## # V52 [m], V53 [m], V54 [m], V55 [m], V56 [m], V57 [m], V58 [m], V59 [m], ## # V60 [m], V61 [m], V62 [m], V63 [m], V64 [m], V65 [m], V66 [m], V67 [m], ## # V68 [m], V69 [m], V70 [m], V71 [m], V72 [m], V73 [m], V74 [m], V75 [m], ## # V76 [m], V77 [m], V78 [m], V79 [m], V80 [m], V81 [m], V82 [m], V83 [m], ## # V84 [m], V85 [m], V86 [m], V87 [m], V88 [m], V89 [m], V90 [m], V91 [m], ## # V92 [m], V93 [m], V94 [m], V95 [m], V96 [m], V97 [m], V98 [m], V99 [m], ## # V100 [m], V101 [m], V102 [m], V103 [m], V104 [m], V105 [m], V106 [m], ## # V107 [m], V108 [m], ... ``` ] --- ## Find Minimum Distance That's all there is concerning the "spatial" part of our data wrangling. From now on, just good old (boring) data crunching to get our distance to the closest hospital. .pull-left[ ```r distance_closest <- distance_matrix %>% # from unit to numeric mutate_all(as.numeric) %>% # identify for each row the minimum # & save in variable mutate(., dist_closest_hospital = (apply(., 1, min))) %>% # select only column # containing smallest distance dplyr::select(., dist_closest_hospital) ``` ] .pull-right[ ``` ## # A tibble: 1,000 x 1 ## dist_closest_hospital ## <dbl> ## 1 1229. ## 2 8078. ## 3 5975. ## 4 5266. ## 5 10890. ## 6 921. ## 7 12805. ## 8 3718. ## 9 4799. ## 10 2997. ## # ... with 990 more rows ``` ] --- ## Get all the information together! Again, I prefer to work with kilometers rather than meters. And I want to add our new variable to the other spatial information we already prepared. Luckily, I know that the spatial information table has the same length and order as the fake coordinates. Otherwise, it would be wise to bind via the original coordinate data frame we used for `st_distance`. .pull-left[ ```r spatial_information <- distance_closest %>% # get kilometer mutate(dist_closest_hospital = dist_closest_hospital/1000 ) %>% # bind columns with spatial information # only bind with other data set than the # original coordinates when you are 100 # percent sure it's same length and order! bind_cols(spatial_information, .) ``` ] .pull-right[ ``` ## id_2 district_id pop_dens incidence_diff dist_closest_hospital ## 1 ihkXs9ejBD 5166 530.3000 -0.1728428 1.22948507 ## 2 bN2W1BpZKx 5570 210.6921 20.7559518 8.07835206 ## 3 wAbDkMovWz 5958 132.5497 -2.5818931 5.97517502 ## 4 R4eIulul4z 5570 210.6921 20.7559518 5.26598082 ## 5 XOvF2ZuGg1 5966 188.2262 -8.1154907 10.88975031 ## 6 EPuILKVeoq 5978 727.5190 4.6643744 0.92144065 ## 7 39TfAAxmme 5958 132.5497 -2.5818931 12.80473402 ## 8 2tolhpgrNl 5962 386.5767 16.6148759 3.71847218 ## 9 nGgofAl6iC 5119 2720.6816 15.5098081 4.79946609 ## 10 4sXgiH1ydA 5315 2675.5176 -7.1701807 2.99738041 ## 11 zF5RjK8V7v 5766 278.9389 23.7113643 8.49118974 ## 12 jOO2VHwycb 5170 441.2735 -14.1049414 9.33542638 ## 13 aM5jtujeJR 5162 783.2699 -11.8979375 7.54012015 ## 14 RFqtgUJmqR 5378 646.4502 4.5788380 2.27925514 ## 15 8QYMhEdKUV 5358 281.3451 19.3048880 12.07881693 ## 16 9MTwgPDLpH 5366 154.9113 -21.9525779 5.87910515 ## 17 30Bi23QI6u 5566 249.6467 -29.8314226 12.15129951 ## 18 JtRdole7nK 5766 278.9389 23.7113643 4.13983356 ## 19 R3lbT7Rruc 5913 2103.1495 -8.9819608 1.99645476 ## 20 NrGmMnwYQR 5112 2139.4596 -5.6876479 3.15762145 ## 21 WfIXk6eiST 5382 521.3532 -21.2497748 14.86562279 ## 22 M19UVtRjIp 5962 386.5767 16.6148759 1.31510777 ## 23 wWE2klWU84 5958 132.5497 -2.5818931 6.93143855 ## 24 g1YZKCfom9 5170 441.2735 -14.1049414 3.71150608 ## 25 Ctpu1AkZNX 5966 188.2262 -8.1154907 9.41098154 ## 26 tvsw0DcPnL 5754 376.4177 7.2145259 11.10200219 ## 27 y3jQ6WZ0MJ 5554 261.2675 -29.6786278 4.78599016 ## 28 pHsLA8ji19 5966 188.2262 -8.1154907 14.69207285 ## 29 zJIDJ2hsSr 5562 806.6064 17.5484224 11.96619577 ## 30 E311lYIbSG 5374 296.2820 12.3763156 18.85169805 ## 31 nAuSmBzE0e 5970 244.2126 -9.5891672 16.38367342 ## 32 5cuKs37UfT 5962 386.5767 16.6148759 5.79715070 ## 33 52bsUCSzrI 5362 669.2841 -24.4535517 4.06641638 ## 34 fTiCkROohX 5770 269.4442 17.2340828 18.82483309 ## 35 hp5sYBnxaz 5754 376.4177 7.2145259 11.80708828 ## 36 1foj1hkWPR 5962 386.5767 16.6148759 7.48268343 ## 37 lRagmzRqTk 5358 281.3451 19.3048880 8.84995325 ## 38 mM6EWST3C3 5978 727.5190 4.6643744 1.39886444 ## 39 x7RBN9jtO7 5358 281.3451 19.3048880 8.56021560 ## 40 jDtxOGb0XC 5366 154.9113 -21.9525779 12.14421969 ## 41 QbyUdmkLSQ 5554 261.2675 -29.6786278 1.49291470 ## 42 X62irYEpgw 5515 1038.6794 -53.5148313 1.88319266 ## 43 igjSIMzcXl 5974 226.9904 -15.2572507 5.18265918 ## 44 cY4ZT6M4FA 5334 790.8127 -3.0209752 3.27014352 ## 45 WbJnTxQLDi 5766 278.9389 23.7113643 11.87283414 ## 46 TrGRGMLbrI 5166 530.3000 -0.1728428 14.73560967 ## 47 laDf5yuNsA 5374 296.2820 12.3763156 1.80226696 ## 48 Wm037rL6lF 5362 669.2841 -24.4535517 2.39156791 ## 49 KqRssAE9Qz 5766 278.9389 23.7113643 6.09562573 ## 50 qCtSpVhAGN 5558 198.3087 -48.8729749 8.20480655 ## 51 jbjezVYKW8 5554 261.2675 -29.6786278 6.63521918 ## 52 tEZGTCmm99 5116 1528.8487 -4.9014571 0.25984843 ## 53 ESJLpnbjwu 5154 253.3367 -13.5318504 6.18513496 ## 54 fgmbUoOgj9 5770 269.4442 17.2340828 24.49765923 ## 55 nOZWDKTT3x 5554 261.2675 -29.6786278 9.77509226 ## 56 Et637ljumR 5554 261.2675 -29.6786278 5.74789403 ## 57 H0QWfWotUk 5974 226.9904 -15.2572507 3.11852915 ## 58 Lu5U9fMlYD 5315 2675.5176 -7.1701807 3.02555198 ## 59 OIg9k8zMiB 5366 154.9113 -21.9525779 1.56669630 ## 60 KpJ1M6H2Rv 5358 281.3451 19.3048880 4.96396798 ## 61 cTaRypYlFS 5378 646.4502 4.5788380 4.21832444 ## 62 Aqq5iIsyAe 5358 281.3451 19.3048880 10.05381740 ## 63 wkPYzCKRji 5370 407.4504 15.6306340 3.65832205 ## 64 YyjyRnVqMb 5382 521.3532 -21.2497748 2.32186794 ## 65 fQCAcea63L 5334 790.8127 -3.0209752 2.59983175 ## 66 MFcUdP4HmS 5554 261.2675 -29.6786278 5.98759150 ## 67 hmEBDYfZle 5362 669.2841 -24.4535517 0.79024974 ## 68 S03nfDNzaK 5154 253.3367 -13.5318504 7.34717410 ## 69 9QUO4rWAcS 5774 246.8979 -23.4800676 9.03677080 ## 70 jS1kdc4g92 5711 1290.0100 -16.7802485 1.90687251 ## 71 gNbC50VXYJ 5566 249.6467 -29.8314226 8.79466906 ## 72 fJmzP566wG 5117 1869.6726 -25.5501424 2.29406716 ## 73 BTGtrPfEJh 5515 1038.6794 -53.5148313 1.71717397 ## 74 sHlIxd9BiW 5314 2327.6610 12.3858429 2.43846403 ## 75 MLXfykiUff 5958 132.5497 -2.5818931 16.64114083 ## 76 3NJxkN7llR 5154 253.3367 -13.5318504 6.46405149 ## 77 OlAFjehZUU 5558 198.3087 -48.8729749 7.54891506 ## 78 ubNpC1kA7s 5914 1176.5770 125.4923878 4.66986333 ## 79 5neSIUdP60 5762 116.8019 69.6485964 17.84694079 ## 80 R3jB7rpgeW 5374 296.2820 12.3763156 17.62819028 ## 81 0YauKfSE7z 5978 727.5190 4.6643744 0.52020437 ## 82 bcY7K7TQNf 5758 555.8661 -0.2993189 8.05014894 ## 83 Zz8sUReaNF 5962 386.5767 16.6148759 3.95153750 ## 84 eVkuMsE424 5566 249.6467 -29.8314226 8.14752435 ## 85 msxxEZ4Tja 5170 441.2735 -14.1049414 10.25140208 ## 86 BavyspqNRz 5154 253.3367 -13.5318504 5.38932305 ## 87 0l9FI6UufF 5382 521.3532 -21.2497748 1.34879454 ## 88 8VlSLYistJ 5974 226.9904 -15.2572507 8.20480637 ## 89 P0skE3e2DT 5374 296.2820 12.3763156 2.83244068 ## 90 gIkKRrzMa5 5382 521.3532 -21.2497748 5.66527958 ## 91 VBjOHiF5Nc 5170 441.2735 -14.1049414 6.23701973 ## 92 r9AiEYKjut 5154 253.3367 -13.5318504 5.90302000 ## 93 Utb0ew6fmP 5913 2103.1495 -8.9819608 3.64347357 ## 94 N3aKBpPIgf 5316 2078.1543 13.0547292 2.54126359 ## 95 c1s0m5g2tF 5758 555.8661 -0.2993189 4.26622788 ## 96 ufgmLaJEK0 5970 244.2126 -9.5891672 6.15314483 ## 97 MREqvfdATt 5754 376.4177 7.2145259 9.33525076 ## 98 Q6c2ibqfTR 5562 806.6064 17.5484224 2.07934985 ## 99 0HIq0I4Qrz 5913 2103.1495 -8.9819608 1.94658428 ## 100 0YtkZjrml4 5154 253.3367 -13.5318504 3.81855872 ## 101 XqXa43qytO 5554 261.2675 -29.6786278 8.18164801 ## 102 dgiyBZaP8k 5566 249.6467 -29.8314226 6.66338847 ## 103 sYoNbawk0T 5554 261.2675 -29.6786278 8.19509284 ## 104 6gZNr74Xwa 5570 210.6921 20.7559518 8.50908092 ## 105 0XSzWxke1F 5554 261.2675 -29.6786278 5.75080532 ## 106 TAisBA9PQB 5974 226.9904 -15.2572507 1.08143747 ## 107 SorDO9kZt4 5774 246.8979 -23.4800676 4.75894805 ## 108 0IYR2JhMfi 5974 226.9904 -15.2572507 3.64957777 ## 109 eOrslaOVwl 5566 249.6467 -29.8314226 12.93322928 ## 110 2s46FecTI9 5113 2768.3700 21.4346329 1.74613711 ## 111 1H06xn879t 5770 269.4442 17.2340828 6.44877958 ## 112 KNXfCZStPk 5915 790.3722 15.2141104 6.10273699 ## 113 kY0MMPIQb5 5374 296.2820 12.3763156 5.59027711 ## 114 QDcpUg6Zpk 5366 154.9113 -21.9525779 18.38619885 ## 115 D52915XNny 5958 132.5497 -2.5818931 4.91391508 ## 116 i37ePMZQEL 5554 261.2675 -29.6786278 3.72551298 ## 117 lN6PXH90gl 5711 1290.0100 -16.7802485 4.10914851 ## 118 5qpIU29ccJ 5566 249.6467 -29.8314226 11.86078688 ## 119 UyWhuGJnty 5974 226.9904 -15.2572507 8.03732491 ## 120 dejYFt38Cl 5382 521.3532 -21.2497748 11.08122500 ## 121 vp6HTAXw2V 5774 246.8979 -23.4800676 10.19605472 ## 122 1TgkUY4Do9 5562 806.6064 17.5484224 1.22247832 ## 123 KkwM8zBbdK 5554 261.2675 -29.6786278 7.92344962 ## 124 NnW5Oy5kHr 5554 261.2675 -29.6786278 9.76506665 ## 125 KlY4xyYAOL 5515 1038.6794 -53.5148313 2.04010134 ## 126 9jOCbSn3Ip 5566 249.6467 -29.8314226 9.36070052 ## 127 3wS3biDpSY 5566 249.6467 -29.8314226 5.77066863 ## 128 pqlvfh51u1 5962 386.5767 16.6148759 4.00816444 ## 129 kd6nldEgvB 5370 407.4504 15.6306340 7.73450619 ## 130 Y1v4rT1599 5570 210.6921 20.7559518 8.67428020 ## 131 FOh8BaXaJ6 5170 441.2735 -14.1049414 8.88289501 ## 132 W8ayhl9wyN 5566 249.6467 -29.8314226 10.91610991 ## 133 OL23Mi0wMj 5366 154.9113 -21.9525779 6.79956010 ## 134 KuEwPwL9R0 5970 244.2126 -9.5891672 4.76607763 ## 135 1A1UYheMa5 5913 2103.1495 -8.9819608 1.70108304 ## 136 kDKyLpOqW8 5974 226.9904 -15.2572507 5.93535389 ## 137 T6SDlEaCM5 5962 386.5767 16.6148759 0.74763220 ## 138 otsRzdIE6q 5111 2859.3977 -13.0128220 1.85485362 ## 139 6PnGqSXYZD 5111 2859.3977 -13.0128220 1.71689134 ## 140 HIYl63icUj 5370 407.4504 15.6306340 2.63202064 ## 141 Ygp6BK5k6B 5170 441.2735 -14.1049414 8.65410821 ## 142 2J6EJPOBTq 5974 226.9904 -15.2572507 9.38431828 ## 143 xWFxWU6TKZ 5362 669.2841 -24.4535517 3.52825132 ## 144 hh1akN9Z8u 5370 407.4504 15.6306340 2.95493162 ## 145 kH8Zeh5FK6 5370 407.4504 15.6306340 6.33325081 ## 146 eduSyeHyFa 5774 246.8979 -23.4800676 9.47309850 ## 147 R3CEG5d71C 5770 269.4442 17.2340828 6.84098514 ## 148 uCzR59t9WO 5124 2108.7618 37.9719569 2.48915426 ## 149 TcgoDKcMA7 5711 1290.0100 -16.7802485 2.03865060 ## 150 Wq0QOD4MQQ 5566 249.6467 -29.8314226 2.90257561 ## 151 9HbN1wgmnv 5774 246.8979 -23.4800676 9.84633983 ## 152 5ZfpOqrDLt 5566 249.6467 -29.8314226 4.68079572 ## 153 YlUYqOCDd1 5119 2720.6816 15.5098081 2.20146758 ## 154 v5s43Nj9VJ 5382 521.3532 -21.2497748 5.02674713 ## 155 N6K2qOSACk 5958 132.5497 -2.5818931 1.88897282 ## 156 V5XNSaOfCe 5382 521.3532 -21.2497748 9.22648904 ## 157 F78gCDxCIp 5382 521.3532 -21.2497748 8.09899290 ## 158 aqugsIoXnN 5770 269.4442 17.2340828 8.24296232 ## 159 V4dW7OBvJU 5954 788.7032 -7.2049774 2.19005628 ## 160 xh4tC0D6wX 5362 669.2841 -24.4535517 5.52732505 ## 161 wqrQE3oStW 5570 210.6921 20.7559518 2.69143734 ## 162 dAM6KfyLh4 5711 1290.0100 -16.7802485 3.37388140 ## 163 lTgxOPMtDr 5970 244.2126 -9.5891672 8.65890951 ## 164 G6tvnam1Cg 5154 253.3367 -13.5318504 1.91476689 ## 165 1Wmxa8Owff 5382 521.3532 -21.2497748 14.14369183 ## 166 vGgxUgv2Y6 5954 788.7032 -7.2049774 2.41076434 ## 167 te5FN1fpjp 5962 386.5767 16.6148759 2.71686574 ## 168 YBgBdjNfGP 5558 198.3087 -48.8729749 9.85910176 ## 169 Z7oQCfrZgR 5362 669.2841 -24.4535517 1.58057617 ## 170 ZCBMvI2651 5570 210.6921 20.7559518 3.97405151 ## 171 1sl6Ddxhxg 5758 555.8661 -0.2993189 5.61908947 ## 172 RGEUDuE8dP 5974 226.9904 -15.2572507 3.17507825 ## 173 e53YLNCcNp 5382 521.3532 -21.2497748 7.64547511 ## 174 fcbu0h6BU5 5358 281.3451 19.3048880 7.08820757 ## 175 ps8lZxctkc 5162 783.2699 -11.8979375 7.04810050 ## 176 JntvToh5Yj 5374 296.2820 12.3763156 5.29309865 ## 177 0ITmNOeRFM 5766 278.9389 23.7113643 8.75945364 ## 178 cm00QUJpiW 5958 132.5497 -2.5818931 9.11859961 ## 179 ZA1TeUFRta 5562 806.6064 17.5484224 5.22822628 ## 180 AiEzaLmHJG 5154 253.3367 -13.5318504 9.39652653 ## 181 wbGqwZUHA6 5554 261.2675 -29.6786278 13.00779648 ## 182 OrFbz5NcFg 5954 788.7032 -7.2049774 3.60024428 ## 183 bbxWY0SSfO 5170 441.2735 -14.1049414 10.15891902 ## 184 RIGrbpnJiq 5974 226.9904 -15.2572507 9.28629207 ## 185 D23cqck0fb 5170 441.2735 -14.1049414 1.44568806 ## 186 6oT4h5r2hl 5382 521.3532 -21.2497748 3.98751406 ## 187 UH57dp0Fcl 5124 2108.7618 37.9719569 3.99801840 ## 188 CKhgfepTQ6 5374 296.2820 12.3763156 1.79745342 ## 189 mHZblCzJXj 5315 2675.5176 -7.1701807 5.13880080 ## 190 gHSSywgKOx 5978 727.5190 4.6643744 3.35988389 ## 191 GSa0mmZcuh 5558 198.3087 -48.8729749 3.03184305 ## 192 CrvonybCR7 5554 261.2675 -29.6786278 7.95854723 ## 193 kHSNKEpVsj 5370 407.4504 15.6306340 2.86319836 ## 194 WBR3ViGGaA 5170 441.2735 -14.1049414 8.44205910 ## 195 8dfAHuyDcD 5774 246.8979 -23.4800676 8.74170696 ## 196 EQOrXoqWPS 5958 132.5497 -2.5818931 4.29917391 ## 197 ljusSs9v5y 5762 116.8019 69.6485964 14.73921487 ## 198 SZWDVKPd1N 5974 226.9904 -15.2572507 6.87483353 ## 199 Mh0bnZ76ip 5515 1038.6794 -53.5148313 2.33078008 ## 200 Rc36NeZ9HM 5382 521.3532 -21.2497748 3.36137836 ## 201 XhBBp1XlkD 5766 278.9389 23.7113643 1.55740513 ## 202 DsZkIVW5nG 5970 244.2126 -9.5891672 15.88265007 ## 203 DI2tCf5WXF 5170 441.2735 -14.1049414 3.76009866 ## 204 KwtTA0519V 5758 555.8661 -0.2993189 4.50059300 ## 205 voQU1xC62z 5974 226.9904 -15.2572507 10.36699883 ## 206 gDtV5CiQxQ 5366 154.9113 -21.9525779 8.82818258 ## 207 LfFdFCPFr9 5374 296.2820 12.3763156 5.85028582 ## 208 8LEI8Szn7j 5158 1190.5861 18.5311776 1.31347888 ## 209 Wv8Qejj3MO 5382 521.3532 -21.2497748 7.21136537 ## 210 eXnzeRKXXf 5374 296.2820 12.3763156 1.39374421 ## 211 PWF8SxYQ0n 5754 376.4177 7.2145259 16.47795369 ## 212 CMfkJ5JtVA 5382 521.3532 -21.2497748 9.16110799 ## 213 ctJOqNeNZq 5170 441.2735 -14.1049414 3.38752862 ## 214 GmQN0kikYW 5358 281.3451 19.3048880 7.37081249 ## 215 mzonBeFgsl 5170 441.2735 -14.1049414 1.29360937 ## 216 M65Nhc81D1 5154 253.3367 -13.5318504 2.62917969 ## 217 yQwRkx7bh8 5974 226.9904 -15.2572507 11.07723188 ## 218 DPqe5YFpDe 5170 441.2735 -14.1049414 10.79067800 ## 219 YB4P1UNLJC 5762 116.8019 69.6485964 4.00676622 ## 220 QLMpkPfMOj 5358 281.3451 19.3048880 3.48145398 ## 221 saQfa3LBBZ 5974 226.9904 -15.2572507 2.31984025 ## 222 cHptPqTLBJ 5915 790.3722 15.2141104 3.76219036 ## 223 vOF4SsRx9Q 5754 376.4177 7.2145259 15.38094283 ## 224 O190jRQkSB 5954 788.7032 -7.2049774 1.70912793 ## 225 nLuJr6UmJf 5770 269.4442 17.2340828 5.05343839 ## 226 FOeYLQsZCo 5774 246.8979 -23.4800676 11.96729497 ## 227 fhvjJXAn94 5966 188.2262 -8.1154907 9.16716632 ## 228 s4q5I8u7iI 5114 1664.4384 12.4371718 2.96394776 ## 229 6FVHBxgUB7 5774 246.8979 -23.4800676 5.53865868 ## 230 zPQvABy2kt 5758 555.8661 -0.2993189 5.72445527 ## 231 UqvSrojKTa 5162 783.2699 -11.8979375 3.56477635 ## 232 6NkEBczRvq 5954 788.7032 -7.2049774 3.41235552 ## 233 8AjnnFLLCY 5374 296.2820 12.3763156 10.67344710 ## 234 Lfn9FmukyD 5774 246.8979 -23.4800676 13.60080024 ## 235 bRp35eg77M 5166 530.3000 -0.1728428 3.49988529 ## 236 bp4rlQRRh7 5374 296.2820 12.3763156 2.68449742 ## 237 tI0djC7KrM 5958 132.5497 -2.5818931 11.41024079 ## 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713 rTvtC1dgZi 5558 198.3087 -48.8729749 1.75463238 ## 714 nIyvntbnTr 5766 278.9389 23.7113643 8.12919912 ## 715 IMttpmAMzv 5515 1038.6794 -53.5148313 4.31021325 ## 716 ZneJLXAw3U 5570 210.6921 20.7559518 7.98159655 ## 717 F3WcL4wE0D 5966 188.2262 -8.1154907 14.31977667 ## 718 sRbOicgQLi 5154 253.3367 -13.5318504 6.13963043 ## 719 lGFMMVtBw8 5962 386.5767 16.6148759 4.19008699 ## 720 Ss1NiY5P04 5970 244.2126 -9.5891672 13.72510443 ## 721 WrGVoNq5LU 5962 386.5767 16.6148759 9.52440885 ## 722 JCgg61qsUB 5962 386.5767 16.6148759 7.98708417 ## 723 uCUMgBsSTx 5566 249.6467 -29.8314226 8.28734637 ## 724 lHqDrxCFjf 5958 132.5497 -2.5818931 2.43371813 ## 725 t1dS5ZMJb0 5766 278.9389 23.7113643 7.84740416 ## 726 ukzjIvqOaz 5974 226.9904 -15.2572507 7.63903541 ## 727 AKoClH1ffL 5166 530.3000 -0.1728428 4.84784166 ## 728 NNEuGukSbM 5362 669.2841 -24.4535517 6.02606052 ## 729 x0TkVNmbDQ 5978 727.5190 4.6643744 6.46634548 ## 730 tMnhP1urwm 5158 1190.5861 18.5311776 0.05502372 ## 731 nWtshTz312 5366 154.9113 -21.9525779 7.07651125 ## 732 yL2Wea4oS1 5962 386.5767 16.6148759 7.27606042 ## 733 Z7TSfO4CLI 5334 790.8127 -3.0209752 6.88941644 ## 734 tlArGhohtr 5170 441.2735 -14.1049414 3.19241559 ## 735 PajkR9CO9Z 5962 386.5767 16.6148759 9.74165781 ## 736 kSUeD7Nlxu 5762 116.8019 69.6485964 18.98018036 ## 737 bWxERYKcrx 5915 790.3722 15.2141104 3.44634092 ## 738 jVOE7upYYZ 5970 244.2126 -9.5891672 12.52567399 ## 739 D76yUg215D 5570 210.6921 20.7559518 9.44214494 ## 740 VduYNrp4BS 5958 132.5497 -2.5818931 12.16502111 ## 741 b55XKChHIf 5315 2675.5176 -7.1701807 2.17939115 ## 742 0PXJ9bi9e4 5758 555.8661 -0.2993189 5.71528957 ## 743 CTlxqWc9fd 5954 788.7032 -7.2049774 1.87345655 ## 744 zxOAwVnCN1 5112 2139.4596 -5.6876479 4.86854298 ## 745 EPljOummJQ 5558 198.3087 -48.8729749 4.43692310 ## 746 RNAODLr4gt 5970 244.2126 -9.5891672 16.68354837 ## 747 uHBUe3KPtI 5378 646.4502 4.5788380 4.56585510 ## 748 xYbJvqrYAs 5378 646.4502 4.5788380 4.05026105 ## 749 jDkghHywR4 5914 1176.5770 125.4923878 2.73974125 ## 750 vY4SUkiGuF 5754 376.4177 7.2145259 4.26254722 ## 751 dsSb9ukWZJ 5154 253.3367 -13.5318504 13.09480404 ## 752 8JzY70nscp 5774 246.8979 -23.4800676 12.95867198 ## 753 t5dCgXr0uw 5158 1190.5861 18.5311776 1.28369481 ## 754 IjED0KclKh 5566 249.6467 -29.8314226 5.32476675 ## 755 Q5eYp1tllD 5370 407.4504 15.6306340 7.25788303 ## 756 Mh0vRPNsfu 5362 669.2841 -24.4535517 6.65714898 ## 757 tHJSC3U8Jo 5166 530.3000 -0.1728428 5.59098110 ## 758 UrwWbKE1Lw 5916 3037.6698 26.6358873 2.15822858 ## 759 R2KkEeESP4 5966 188.2262 -8.1154907 16.22344010 ## 760 LqIvYYi3AP 5154 253.3367 -13.5318504 5.79370225 ## 761 hjKoYBKHkB 5770 269.4442 17.2340828 7.33318340 ## 762 YBtScOmAjQ 5974 226.9904 -15.2572507 4.58452246 ## 763 0Us5nEjZVK 5154 253.3367 -13.5318504 10.88959509 ## 764 ObY5oHgIJX 5378 646.4502 4.5788380 6.61160265 ## 765 o3rI7pSfHB 5958 132.5497 -2.5818931 5.72285435 ## 766 1N7ebZ9UPU 5562 806.6064 17.5484224 3.98847994 ## 767 XjpyAUWh31 5770 269.4442 17.2340828 22.20881977 ## 768 R7JBU79d3F 5774 246.8979 -23.4800676 3.55304178 ## 769 ES5aZupKbj 5974 226.9904 -15.2572507 4.43878330 ## 770 iNXq4rFx87 5120 1503.0525 -18.2394110 1.61463029 ## 771 DP6U2JJoTo 5974 226.9904 -15.2572507 2.94574831 ## 772 aQp4MALjK9 5558 198.3087 -48.8729749 7.16700620 ## 773 RRWazd8aYK 5374 296.2820 12.3763156 5.76108059 ## 774 XBXOyjnsHw 5358 281.3451 19.3048880 4.01187370 ## 775 0KsJYER8k9 5370 407.4504 15.6306340 8.26875151 ## 776 mVRj0sokHX 5162 783.2699 -11.8979375 7.01605944 ## 777 1yy8PT29Vr 5754 376.4177 7.2145259 10.25601246 ## 778 3hy33xMbKS 5774 246.8979 -23.4800676 10.33440891 ## 779 mH3rpOccR6 5958 132.5497 -2.5818931 10.30022431 ## 780 fgtrfsM37Z 5570 210.6921 20.7559518 2.51381186 ## 781 LoniBIKHzW 5166 530.3000 -0.1728428 3.24008694 ## 782 Kuzr7ZLnjd 5966 188.2262 -8.1154907 12.15664971 ## 783 jf2uCu8qFt 5758 555.8661 -0.2993189 6.65758296 ## 784 ALER1AiBqk 5970 244.2126 -9.5891672 7.68150034 ## 785 Z83vYsrWVc 5762 116.8019 69.6485964 12.21743801 ## 786 Dxmk7YtHS8 5170 441.2735 -14.1049414 5.72764653 ## 787 jQFWAmAhSo 5566 249.6467 -29.8314226 6.17214530 ## 788 pwtZGTB0e0 5566 249.6467 -29.8314226 8.17238973 ## 789 ek9qfdCjQj 5366 154.9113 -21.9525779 10.59332485 ## 790 CEpo1nvpb0 5358 281.3451 19.3048880 7.21491350 ## 791 5wJI2Z7MFh 5558 198.3087 -48.8729749 4.33719844 ## 792 Yprj3UwiVL 5154 253.3367 -13.5318504 7.36110787 ## 793 9HhTSgyYCq 5711 1290.0100 -16.7802485 4.27461498 ## 794 iZoynjVruh 5974 226.9904 -15.2572507 8.55034613 ## 795 P9zrjOmyyJ 5770 269.4442 17.2340828 8.11623444 ## 796 qECKR5ZTCl 5954 788.7032 -7.2049774 1.29346958 ## 797 hDjchOuM9q 5766 278.9389 23.7113643 6.03368457 ## 798 bbONtS6ZrG 5334 790.8127 -3.0209752 3.08224638 ## 799 ZDTk0T0elI 5378 646.4502 4.5788380 6.81035428 ## 800 a8PvhMLH8r 5378 646.4502 4.5788380 2.10103202 ## 801 uuRa9IHOIP 5382 521.3532 -21.2497748 6.44228928 ## 802 xfcQq0Iw8o 5554 261.2675 -29.6786278 7.90821515 ## 803 bvYH7IPdCB 5754 376.4177 7.2145259 3.94142835 ## 804 mlxahTh4oZ 5362 669.2841 -24.4535517 7.29203745 ## 805 GSz0sjpzK4 5154 253.3367 -13.5318504 5.19640487 ## 806 M5TruxDu9Y 5366 154.9113 -21.9525779 7.70850303 ## 807 1VyU25eaJE 5958 132.5497 -2.5818931 2.08199871 ## 808 24lKDS9pli 5122 1789.3801 2.0427886 2.23284670 ## 809 KERlPe46kL 5970 244.2126 -9.5891672 1.31472178 ## 810 6WJT9AjHfD 5911 2524.4746 1.8510400 3.05694617 ## 811 DzYg6fAR0B 5570 210.6921 20.7559518 8.07137079 ## 812 siVRTULbGb 5366 154.9113 -21.9525779 11.93479041 ## 813 hvgywwrA33 5558 198.3087 -48.8729749 10.23780801 ## 814 LaD3MBfLIM 5366 154.9113 -21.9525779 3.96913120 ## 815 f0MERx59xt 5962 386.5767 16.6148759 2.00099142 ## 816 YPXJVnEn3Q 5762 116.8019 69.6485964 8.40251270 ## 817 5qrvPZYZDk 5170 441.2735 -14.1049414 1.73657936 ## 818 mQmCG2RcpJ 5766 278.9389 23.7113643 7.84452036 ## 819 m57rZwDnFC 5316 2078.1543 13.0547292 4.71329240 ## 820 HQ4qsO0Cgv 5570 210.6921 20.7559518 9.50279367 ## 821 KwEj2vl97n 5170 441.2735 -14.1049414 6.07847350 ## 822 rCiUhEsiAh 5970 244.2126 -9.5891672 17.46628185 ## 823 N0LRKQ46jk 5962 386.5767 16.6148759 9.81844489 ## 824 4vqPvlYCD6 5962 386.5767 16.6148759 4.47239772 ## 825 6l1907gSGE 5166 530.3000 -0.1728428 3.00608553 ## 826 FAzeWuikpD 5170 441.2735 -14.1049414 12.31987488 ## 827 hFpMIKVhzc 5770 269.4442 17.2340828 9.52302389 ## 828 46AOqEHD0I 5958 132.5497 -2.5818931 3.56112698 ## 829 sbKs1GAd4L 5762 116.8019 69.6485964 14.57338133 ## 830 SxcbGBL1WV 5554 261.2675 -29.6786278 6.04480329 ## 831 ldJWFsvkqO 5378 646.4502 4.5788380 5.46674010 ## 832 71nn8LyOR4 5570 210.6921 20.7559518 1.16364101 ## 833 C8PkgGN94A 5558 198.3087 -48.8729749 6.40019106 ## 834 dLywhwzp3y 5570 210.6921 20.7559518 7.37729584 ## 835 DQuC3ilgLy 5166 530.3000 -0.1728428 2.17253271 ## 836 FY9fYVopef 5570 210.6921 20.7559518 12.44202801 ## 837 3DhSVRhaMx 5974 226.9904 -15.2572507 2.91949611 ## 838 qr0YXrm6UJ 5958 132.5497 -2.5818931 9.86207038 ## 839 AC4vjFfeFo 5166 530.3000 -0.1728428 2.93283073 ## 840 V9RZVn3PXY 5366 154.9113 -21.9525779 11.93844361 ## 841 nMCjfJOB46 5370 407.4504 15.6306340 5.88644907 ## 842 EZvXK4IBXn 5766 278.9389 23.7113643 8.49233010 ## 843 NNlI4GJZ8I 5558 198.3087 -48.8729749 6.91351049 ## 844 0OQX3TsAs2 5154 253.3367 -13.5318504 5.97282037 ## 845 Am5Ee94H05 5762 116.8019 69.6485964 16.22825946 ## 846 T0hfD16jsQ 5515 1038.6794 -53.5148313 2.47029247 ## 847 l1IEhY03vH 5566 249.6467 -29.8314226 7.13267033 ## 848 KSApp0DH5j 5966 188.2262 -8.1154907 5.56813534 ## 849 uYKigekvfn 5962 386.5767 16.6148759 4.70416865 ## 850 XHz7I4gqkx 5562 806.6064 17.5484224 1.65327622 ## 851 zBVDZ130ZN 5774 246.8979 -23.4800676 4.99232151 ## 852 cKY15xG4pf 5374 296.2820 12.3763156 13.06271734 ## 853 U5mbqG3aOn 5958 132.5497 -2.5818931 11.99940080 ## 854 LGXc99jlcO 5962 386.5767 16.6148759 7.13894352 ## 855 350jTGrhv6 5762 116.8019 69.6485964 7.20654516 ## 856 XYzahsIAi0 5758 555.8661 -0.2993189 5.33464425 ## 857 gSLQD1pqmj 5154 253.3367 -13.5318504 9.52404355 ## 858 AG4TnArZPB 5958 132.5497 -2.5818931 7.75743333 ## 859 fU2Eo8z2VS 5158 1190.5861 18.5311776 1.63311581 ## 860 JcQgjVIhYK 5374 296.2820 12.3763156 7.71753505 ## 861 2SZyX1bawv 5962 386.5767 16.6148759 8.49914413 ## 862 bviwKIda7w 5154 253.3367 -13.5318504 4.21885483 ## 863 mujP19Tbly 5116 1528.8487 -4.9014571 5.57113822 ## 864 jKsSPTia7b 5770 269.4442 17.2340828 19.50025973 ## 865 7GgbcVpU6c 5112 2139.4596 -5.6876479 0.83675597 ## 866 lOIuZRDSOM 5382 521.3532 -21.2497748 3.81454119 ## 867 6lhTjXU4vK 5366 154.9113 -21.9525779 26.48170017 ## 868 2R1iGQLaT5 5762 116.8019 69.6485964 2.95153624 ## 869 qyl2LNkPkl 5562 806.6064 17.5484224 2.68307739 ## 870 PLRl5g5C1V 5558 198.3087 -48.8729749 1.95940962 ## 871 uIFiWXvhzZ 5366 154.9113 -21.9525779 20.01141231 ## 872 aMAJspqXyn 5958 132.5497 -2.5818931 6.13448642 ## 873 5qk0V8Bv2Z 5378 646.4502 4.5788380 2.47441687 ## 874 yX0AwyMPnm 5754 376.4177 7.2145259 12.22134660 ## 875 PnEjCcfW7x 5770 269.4442 17.2340828 0.66120845 ## 876 Ub0OAfbLrx 5558 198.3087 -48.8729749 5.67076996 ## 877 w5FG6OHAm0 5382 521.3532 -21.2497748 5.02173209 ## 878 WvZjn33XSD 5166 530.3000 -0.1728428 2.13963352 ## 879 wEeyLwXOk3 5962 386.5767 16.6148759 7.00104213 ## 880 RBaDIbZryZ 5954 788.7032 -7.2049774 5.85221431 ## 881 hmqvhnCtKv 5162 783.2699 -11.8979375 2.48394457 ## 882 lsFdELodlA 5754 376.4177 7.2145259 7.92508972 ## 883 ev0JaDX4pt 5382 521.3532 -21.2497748 3.68890735 ## 884 V4kIrNKq2s 5566 249.6467 -29.8314226 8.21648963 ## 885 jupVEK7VRE 5154 253.3367 -13.5318504 5.04216598 ## 886 pKX4uIoYCE 5562 806.6064 17.5484224 5.09056740 ## 887 89pWBzgy2V 5970 244.2126 -9.5891672 5.09754726 ## 888 H5i4cRICeP 5334 790.8127 -3.0209752 3.81503649 ## 889 h6T9NyyH6B 5558 198.3087 -48.8729749 5.42244135 ## 890 QSWMqtvGFD 5154 253.3367 -13.5318504 9.29131659 ## 891 J4IMtzldlU 5958 132.5497 -2.5818931 3.17697256 ## 892 NuKVwI9hef 5370 407.4504 15.6306340 5.33475184 ## 893 RmCNzgOAwo 5913 2103.1495 -8.9819608 3.28841379 ## 894 zHYZ65VZS2 5382 521.3532 -21.2497748 10.18686321 ## 895 l51IxuazAV 5566 249.6467 -29.8314226 10.77464179 ## 896 xybiwEz7yh 5978 727.5190 4.6643744 4.13675483 ## 897 FqetDbdx7G 5758 555.8661 -0.2993189 5.12429627 ## 898 OmlR4cJBiz 5370 407.4504 15.6306340 3.88081875 ## 899 9kGptiFUrA 5566 249.6467 -29.8314226 8.20914184 ## 900 WgTZgikUD7 5170 441.2735 -14.1049414 11.72191387 ## 901 km2iGpzId4 5370 407.4504 15.6306340 2.22448854 ## 902 oQMtRyqB6v 5566 249.6467 -29.8314226 3.05264504 ## 903 Ykgry4HZ7e 5962 386.5767 16.6148759 7.11796054 ## 904 TJx9QwLOu7 5974 226.9904 -15.2572507 8.46319937 ## 905 0qMELx3QiE 5766 278.9389 23.7113643 6.04341298 ## 906 HtLiNitZvQ 5558 198.3087 -48.8729749 4.86592432 ## 907 tP3qFWKTuZ 5970 244.2126 -9.5891672 16.07912202 ## 908 3atHcY2dL7 5358 281.3451 19.3048880 5.72396960 ## 909 liAQQ6ZmcG 5970 244.2126 -9.5891672 12.94980994 ## 910 UuXAD5puEy 5570 210.6921 20.7559518 7.27949809 ## 911 PkJBUT4iOj 5962 386.5767 16.6148759 4.50972263 ## 912 9tzWttrZYk 5378 646.4502 4.5788380 6.96392322 ## 913 3N0z13pwpo 5915 790.3722 15.2141104 1.45684835 ## 914 LwOLYgPKn2 5366 154.9113 -21.9525779 4.19966272 ## 915 tr5WAu7Xr6 5154 253.3367 -13.5318504 1.83638643 ## 916 QHVZW8f8kX 5158 1190.5861 18.5311776 1.50298679 ## 917 u78bZeT98m 5958 132.5497 -2.5818931 4.98638150 ## 918 KcKtoyeovo 5334 790.8127 -3.0209752 9.11698335 ## 919 uobIFxoeXZ 5166 530.3000 -0.1728428 12.09318925 ## 920 aiIrpFYNtI 5762 116.8019 69.6485964 12.79428054 ## 921 sxDTqTCNBv 5154 253.3367 -13.5318504 6.70238572 ## 922 IljHbioO8X 5916 3037.6698 26.6358873 3.60958593 ## 923 I6s4c10Gvu 5166 530.3000 -0.1728428 3.44746075 ## 924 rVXPX5G4qG 5913 2103.1495 -8.9819608 1.03519219 ## 925 O8RJXYVKE2 5966 188.2262 -8.1154907 5.89667308 ## 926 SyrSiTRxIo 5958 132.5497 -2.5818931 10.59002905 ## 927 ED0rITjtZk 5554 261.2675 -29.6786278 5.10806715 ## 928 5LMMVpXSSq 5374 296.2820 12.3763156 5.79981939 ## 929 nHjfhQpdBV 5366 154.9113 -21.9525779 1.45252246 ## 930 vZG3pKeKN3 5966 188.2262 -8.1154907 7.12457526 ## 931 xsc1Lg6QR9 5382 521.3532 -21.2497748 2.44123339 ## 932 dPQLuHeQ4O 5566 249.6467 -29.8314226 9.72939724 ## 933 f9f9br6Dty 5958 132.5497 -2.5818931 5.11661937 ## 934 IF5nfQCY0S 5366 154.9113 -21.9525779 5.39098425 ## 935 74Wgs7V8Id 5114 1664.4384 12.4371718 1.11432195 ## 936 t1Oi2hYNS7 5116 1528.8487 -4.9014571 4.18015940 ## 937 BkfdGqu6Xf 5570 210.6921 20.7559518 12.71581194 ## 938 pL9N37lH5a 5382 521.3532 -21.2497748 3.01762999 ## 939 jl6Y7ld0Gw 5770 269.4442 17.2340828 5.42852377 ## 940 Gl136X2y6n 5978 727.5190 4.6643744 7.20827935 ## 941 aNwh9d5c4t 5558 198.3087 -48.8729749 11.45191875 ## 942 u8krkt1ubx 5978 727.5190 4.6643744 4.13528340 ## 943 iBDKlffMBB 5770 269.4442 17.2340828 4.08790101 ## 944 8xzkMJEWGs 5382 521.3532 -21.2497748 9.34961778 ## 945 HhwaLtZEBF 5382 521.3532 -21.2497748 15.53618735 ## 946 dAOKhTqeK1 5358 281.3451 19.3048880 7.15327111 ## 947 DyvoL79t7O NA NA NA 13.53048558 ## 948 D8Nun56qoj 5766 278.9389 23.7113643 9.31157894 ## 949 wGOP559qph 5366 154.9113 -21.9525779 0.17223709 ## 950 j4ubHe5fSt 5358 281.3451 19.3048880 6.86550092 ## 951 YfBkzDDuY6 5762 116.8019 69.6485964 16.62824547 ## 952 LHQR1nCAbt 5762 116.8019 69.6485964 13.30282534 ## 953 oELEHhj87v 5358 281.3451 19.3048880 9.50773178 ## 954 tBPUtkQ0qE 5762 116.8019 69.6485964 12.74782061 ## 955 pNvmyWNkmZ 5754 376.4177 7.2145259 3.61203158 ## 956 qWdhS7Xj7F 5382 521.3532 -21.2497748 10.08905909 ## 957 E0BOT8GsN6 5562 806.6064 17.5484224 12.72607091 ## 958 08d9EC2QZ3 5558 198.3087 -48.8729749 6.47036637 ## 959 2LZ7l0LhnW 5358 281.3451 19.3048880 6.60409658 ## 960 ex73h9S17y 5962 386.5767 16.6148759 2.24660428 ## 961 oezaHPyXWl 5558 198.3087 -48.8729749 9.33385260 ## 962 RtaGSe88PK 5962 386.5767 16.6148759 6.54273525 ## 963 MEKXQIfsYu 5554 261.2675 -29.6786278 9.15435174 ## 964 h1HPgfUL2y 5366 154.9113 -21.9525779 24.52488792 ## 965 DiE1B7Az12 5358 281.3451 19.3048880 6.84044824 ## 966 STyqN4kS1Y 5374 296.2820 12.3763156 3.06760101 ## 967 vp3zRsBntg 5162 783.2699 -11.8979375 5.14504814 ## 968 RvWSGgDNCD 5554 261.2675 -29.6786278 4.50104292 ## 969 7q6SXwKwGn 5558 198.3087 -48.8729749 3.89910052 ## 970 5n65uwp5pU 5366 154.9113 -21.9525779 10.95320062 ## 971 LFQdbEKsl3 5962 386.5767 16.6148759 5.89550121 ## 972 T0dQKhHcFG 5970 244.2126 -9.5891672 10.93186495 ## 973 T3mTd0VYS7 5315 2675.5176 -7.1701807 5.42085050 ## 974 wcezPPOd3v 5113 2768.3700 21.4346329 1.99760799 ## 975 8b03Seyy45 5958 132.5497 -2.5818931 6.31726991 ## 976 a8zqT6NHw5 5566 249.6467 -29.8314226 2.09182461 ## 977 fOAmRBv1zR 5374 296.2820 12.3763156 18.69122493 ## 978 hYQHVYc4Jp 5374 296.2820 12.3763156 5.58004657 ## 979 uMqUvaRdFg 5374 296.2820 12.3763156 13.88001301 ## 980 1eO16GeSfu 5754 376.4177 7.2145259 8.13123167 ## 981 i0NB6AniWd 5170 441.2735 -14.1049414 4.38035295 ## 982 LD1keBoMUu 5158 1190.5861 18.5311776 0.95054128 ## 983 XtaxYnU1eY 5566 249.6467 -29.8314226 14.43500690 ## 984 ZArHUUJ7jI 5111 2859.3977 -13.0128220 1.49276660 ## 985 I77y8si0lg 5562 806.6064 17.5484224 4.75981191 ## 986 i0Mhtd9Rx7 5970 244.2126 -9.5891672 11.73567386 ## 987 Jpm0rsrcDG 5166 530.3000 -0.1728428 5.30046781 ## 988 eXrE1AZZzy 5554 261.2675 -29.6786278 8.32284615 ## 989 3FB3f1SUrS 5382 521.3532 -21.2497748 4.15900558 ## 990 pKU8Im7age 5970 244.2126 -9.5891672 7.86405216 ## 991 bSbsFdP6gB 5382 521.3532 -21.2497748 7.47123954 ## 992 41HhkMpPbV 5366 154.9113 -21.9525779 17.27703753 ## 993 iE0YIPSsFm 5570 210.6921 20.7559518 12.97588082 ## 994 ASrbQ4LI3s 5515 1038.6794 -53.5148313 4.08654181 ## 995 PIXGqSDFFz 5711 1290.0100 -16.7802485 0.89789909 ## 996 thkMlFVMSD 5958 132.5497 -2.5818931 4.32400300 ## 997 ZCvZTgeavE 5762 116.8019 69.6485964 14.02039778 ## 998 f2HtHf2pMN 5558 198.3087 -48.8729749 1.64904599 ## 999 qLK3GWBg13 5374 296.2820 12.3763156 6.31955452 ## 1000 OUVpAPPYxt 5958 132.5497 -2.5818931 8.03824122 ``` ] --- ## Immigrant Rate Buffers ...and we're not yet done: we still need the immigrant rate in the neighborhood. Let's calculate buffers of 500 meters and add their mean values to our dataset. ```r # download data & create rate immigrant_rate <- z11::z11_get_100m_attribute(STAATSANGE_KURZ_2) * 100 / z11::z11_get_100m_attribute(Einwohner) # set missings immigrant_rate[is.na(immigrant_rate)] <- 0 # spatially link with buffers on the fly spatial_information <- spatial_information %>% dplyr::bind_cols( ., immigrant_buffers = fake_coordinates %>% raster::extract( immigrant_rate, ., buffer = 500, fun = mean ) ) ``` --- ## Join with Fake Burden Score I hope you're not tired to join data tables. Since we care a tiny bit more about data protection than others, we have yet another joining task left: joining the information we received using our (protected) fake coordinates to the actual survey data via the correspondence table. .pull-left[ ```r # last joins for now fake_survey_data_spatial <- # first join the id left_join(correspondence_table, spatial_information, by = "id_2") %>% # drop the fake_coordinate id dplyr::select(. , -id_2) %>% # join the survey data left_join(., fake_survey_data, by = "id") ``` ] .pull-right[ <img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/correlation-plot-1.png" width="75%" style="display: block; margin: auto;" /> ] --- ## ... and when things get complicated? In these presented examples, things are working out quite well. However, you will experience that sometimes things can get a little more complicated. Challenges like not matching geometries cause problems for simple spatial joins and force you to decide how to crop the information. For example, ZIP codes and electoral districts do not match with the administrative districts in Germany. You might also want to advance your measurements and dive deeper into the measurements like densities, distances to lines or polygons, spatial autocorrelation measurements, and neighborhood matrices. All of this is possible using *R/RStudio*! --- class: middle ## Exercise 2_2_1: Spatial Joins [Exercise](https://stefanjuenger.github.io/gesis-workshop-geospatial-techniques-R/exercises/2_2_1_Spatial_Joins_question.html) [Solution](https://stefanjuenger.github.io/gesis-workshop-geospatial-techniques-R/exercises/2_2_1_Spatial_Joins_solution.html) --- class: middle ## Our own applications --- ## Environmental Inequalities (Work In Progress) > Is income associated with fewer environmental disadvantages, and are there differences between German people and people with a migration background? .pull-left[ .small[ Theoretical Framework - Social and Ethnic Inequalities (Crowder & Downey, 2010) - Place Stratification (Lersch, 2013) Data - GGSS 2016 & 2018 - soil sealing & green spaces ] ] .pull-right[ <img src="data:image/png;base64,#./img/fig_linking_buffer_sealing.png" width="65%" style="display: block; margin: auto;" /> .tinyisher[Leibniz Institute of Ecological Urban and Regional Development (2018) / Jünger, 2019] ] --- ## Estimates: Soil Sealing <img src="data:image/png;base64,#./img/fig_prediction_sealing_with_income_interaction_unofficial.png" width="70%" style="display: block; margin: auto;" /> .tinyisher[Data source: GGSS 2016 & 2018; N = 6,117; 95% confidence intervals based on cluster-robust standard errors (sample point); all models control for age, gender, education, household size, german region and survey year interaction, inhabitant size of the municipality, and distance to municipality administration] --- ## Estimates: Green Spaces <img src="data:image/png;base64,#./img/fig_prediction_green_with_income_interaction_unofficial.png" width="70%" style="display: block; margin: auto;" /> .tinyisher[Data source: GGSS 2016 & 2018; N = 6,117; 95% confidence intervals based on cluster-robust standard errors (sample point); all models control for age, gender, education, household size, german region and survey year interaction, inhabitant size of the municipality, and distance to municipality administration] --- ## Left Behind by the State? (Work In Progress) > Are political trust levels affected by the accessibility of public services and infrastructures for citizens? Theoretical Framework - Political Performance-Trust Link (Easton 1965, Hetherington 2005) - Context condition low-intensity information cue: “slow drip of everyday life” (Baybeck & McClurg, 2005; Cho & Rudolph 2008) Data - GGSS 2018 - hospital, school, train station (distance measures) - municipality data --- ## Estimates: Log Distance Models <img src="data:image/png;base64,#./img/fig_mainmodels.png" width="70%" style="display: block; margin: auto;" /> .tinyisher[Data source: GGSS 2018 and Federal Statistical Office 2017. N = 3030, Groups = 152 (Municipalities). Fitted Models: OLS multi-level random effect models. Individual-level controls: income, gender, education, age, personal trust, political interest. Municipality level controls: population density and unemployment. Dependent variable: Trust in government. Survey weights are applied.] --- ## Estimates: Interaction Models <img src="data:image/png;base64,#./img/fig_interactionmodels.png" width="70%" style="display: block; margin: auto;" /> .tinyisher[Data source: GGSS 2018 and Federal Statistical Office 2017. N = 2968, Groups = 152 (Municipalities). Fitted Models: OLS multi-level random effect models. Individual-level controls: income, gender, education, age, personal trust, political interest. Municipality level controls: population density and unemployment. Dependent variable: Trust in government. Survey weights are applied.] --- class: middle ## Lunch Time --- layout: false class: center background-image: url(data:image/png;base64,#./img/the_end.png) background-size: cover .left-column[ <img src="data:image/png;base64,#./img/anne.png" width="60%" style="display: block; margin: auto;" /><img src="data:image/png;base64,#./img/stefan.png" width="60%" style="display: block; margin: auto;" /> ] .right-column[ .left[.small[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;"> [ comment ] <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> [`anne-kathrin.stroppe@gesis.org`](mailto:anne-kathrin.stroppe@gesis.org)] </br> .small[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;"> [ comment ] <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> [`@AStroppe`](https://twitter.com/Astroppe)] </br> </br> .small[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;"> [ comment ] <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> [`stefan.juenger@gesis.org`](mailto:stefan.juenger@gesis.org)] </br> .small[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;"> [ comment ] <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> [`@StefanJuenger`](https://twitter.com/StefanJuenger)]] </br> ]